Face recognition technology is a type of pattern recognition used to identify an individual based on video or still frame images of the individual's face. Typically, a data set of images of the individual's face (i.e., a specific type of pattern) is first collected and then a face image of an unknown individual is evaluated relative to this data set. Traditional face recognition has focused on individual comparisons between single images. As such, if the unknown face image sufficiently matches one or more of the data sets of the known individual, the unknown face image may be classified as that of the individual.
Typically, however, the initial data set of images tends to include substantial variations of state (e.g., in illumination and pose) that make the evaluation with the unknown face image difficult to resolve. In one existing approach, illumination and/or pose variations, for example, in the data set of images are removed by computing illumination and/or invariant images to obtain a more normalized data set. Likewise, an illumination and/or pose invariant versions of the unknown image may also be computed. Unfortunately, such normalization discards or obfuscates unique characteristics of each image.